Google llc (20240281564). Private Federated Learning with Reduced Communication Cost simplified abstract
Contents
Private Federated Learning with Reduced Communication Cost
Organization Name
Inventor(s)
Peter Kairouz of Seattle WA (US)
Christopher Choquette-choo of Sunnyvale CA (US)
Md Enayat Ullah of Baltimore MD (US)
Private Federated Learning with Reduced Communication Cost - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240281564 titled 'Private Federated Learning with Reduced Communication Cost
Simplified Explanation: The patent application introduces new techniques for reducing communication in private federated learning without the need for manual compression rate adjustments. These techniques automatically adjust the compression rate based on training error while ensuring privacy through secure aggregation and differential privacy.
- Secure aggregation and differential privacy used to maintain provable privacy guarantees
- On-the-fly methods adjust compression rate based on training error
- Eliminates the need for manual setting or tuning of compression rates
- Reduces communication in private federated learning
Potential Applications: 1. Privacy-preserving machine learning applications 2. Collaborative learning environments 3. Secure data sharing platforms
Problems Solved: 1. Manual tuning of compression rates in federated learning 2. Ensuring privacy while reducing communication overhead 3. Balancing privacy and efficiency in machine learning models
Benefits: 1. Improved privacy guarantees 2. Reduced communication costs 3. Enhanced efficiency in federated learning environments
Commercial Applications: The technology can be applied in industries such as healthcare, finance, and telecommunications for secure collaborative machine learning projects, leading to cost savings and improved data privacy.
Questions about Private Federated Learning: 1. How does differential privacy enhance privacy guarantees in federated learning? 2. What are the advantages of using on-the-fly compression rate adjustment in machine learning models?
Original Abstract Submitted
new techniques are provided which reduce communication in private federated learning without the need for setting or tuning compression rates. example on-the-fly methods automatically adjust the compression rate based on the error induced during training, while maintaining provable privacy guarantees through the use of secure aggregation and differential privacy.
